Paper Info
Title
Granulation-based symbolic representation of time series and semi-supervised classification
Abstract
We present a semi-supervised time series classification method based on co-training which uses the hidden Markov model (HMM) and one nearest neighbor (1-NN) as two learners. For modeling time series effectively, the symbolization of time series is required and a new granulation-based symbolic representation method is proposed in this paper. First, a granule for each segment of time series is constructed, and then the segments are clustered by spectral clustering applied to the formed similarity matrix. Using four time series datasets from UCR Time Series Data Mining Archive, the experimental results show that proposed symbolic representation works successfully for HMM. Compared with the supervised method, the semi-supervised method can construct accurate classifiers with very little labeled data available.
Year
DOI
Venue
2011
10.1016/j.camwa.2011.09.006
Computers & Mathematics with Applications
Keywords
Field
DocType
new granulation-based symbolic representation,time series,supervised method,ucr time series data,semi-supervised classification,semi-supervised method,granulation-based symbolic representation,accurate classifier,time series datasets,mining archive,proposed symbolic representation,semi-supervised time series classification,spectral clustering,hidden markov model,nearest neighbor,granulation
k-nearest neighbors algorithm,Spectral clustering,Data mining,Time series data mining,Pattern recognition,Artificial intelligence,Granulation,Labeled data,Hidden Markov model,Mathematics,Similarity matrix,Time series classification
Journal
Volume
Issue
ISSN
62
9
0898-1221
Citations 
PageRank 
References 
6
0.44
13
Authors
4
Name
Order
Citations
PageRank
Jun Meng1539.46
LiXia Wu262.47
Xiu-kun Wang3458.99
Tsauyoung Lin4252.71